In Figure , a gallery of fully normalized faces is presented
with the initial source image. The results
demonstrate the strength of the proposed normalization technique.

One curious oddity is the indifference of the algorithm to skin tone or
albedo. Since the ``mean'' face is composed mostly of Caucasians, dark-skinned
individuals lose their distinctive skin colour. Whether this necessarily
has a negative impact on recognition algorithms is uncertain. It is evident,
though, that the faces are still recognizable to a human observer after
illumination normalization.

Overall, we note the regularity with which the faces appear in the normalized
image gallery. The variance in appearance has been constrained to be a
function of individual identity and expression alone, since lighting and pose
have been filtered out of the image. This allows recognition and
classification of identity to be performed on the basis of variance which we
assume is dominated by the identity (not facial expression) of the individual
in the image. This statement is true most of the time since people display a
neutral expression in their daily activities (watching TV, walking, working,
etc.)

On an SGI Indy workstation, the full normalization computation requires under
20 milliseconds for each 7000 pixel mug-shot shown above. The time required
for the computation is almost proportional to the number of pixels in the
output mug-shot being generated. For an 800 pixel mug-shot, the time required
drops to below 2 milliseconds on the SGI Indy. Thus, the above normalization
process is extremely efficient.